Introduction

Relationships are often regarded as the foundations for success in a more global, uncertain and competitive environment (Morgan & Hunt, Reference Möller and Halinen1994), and networks constitute the frameworks for all activities that take place in business relationships (Mattsson, Reference Mattsson1997). This paper specifically focusses on the firm’s network, which directly influences the flow of resources across the firm’s boundaries. The firm’s network consists of its set of direct, dyadic, informal ties and the relationships between these ties, with the firm at the centre of the network as the focal actor (Hite & Hesterly, Reference Hite and Hesterly2001). Informal ties comprise relationships defined as implicit, personal, generic and not fixed by any legal arrangement (Rank, Reference Rank2008). Formal ties that prevail in strategic networks (Jarillo, Reference Jarillo1988) are defined as being explicit, impersonal and functionally specific relationships among firms (Rank, Reference Rank2008).

Researchers have considered two main perspectives in order to study firms’ networks and their effects: the structural and the managerial perspectives. The structural perspective examined how the structure of networks and quality of ties affected resource flow and influenced business behaviour (Hoang & Antoncic, Reference Hoang and Antoncic2003; Batjargal, Reference Batjargal2006). Traditionally, this perspective used three dimensions: the first dimension focussed on the structure of the network and the properties of the position occupied by the agent in the network (structural dimension), the second dimension summarized the characteristics of the agent’s relations, such as confidence and longevity of the link, (relational dimension) and the third dimension measured the value of the resources that networked agents are able to provide (resources dimension) (Hoang & Antoncic, Reference Hoang and Antoncic2003; Batjargal, Reference Batjargal2006). The key intuition in this research is that better socially embedded connected firms have access to valuable resources, because the structure and quality of their network connections shape information access and lead to accelerated trust formation (Burt, Reference Burt1992, Reference Burt2000).

The managerial perspective on networks highlights the importance of what entrepreneurs do to create and shape their business networks. This perspective draws on the fundamental assumption that the mere presence in a network does not create value for firms; rather the value of a network is only realized through the owner–manager’s positive use of the resources contained within the network (Johanson & Vahlne, Reference Johnson and Kaplan2009). For researchers in this perspective, the management of the business network and the management in business networks are key elements in determining firms’ network competence and performance. Consequently, scholars have focussed on how managers’ networking efforts can influence their business networks through churn in their composition. By churn Vissa and Bhagavatula (Reference Vissa and Bhagavatula2012) refer to the change in business network composition caused by the entry of new network contacts and exit of existing network contacts. The importance of having the right counterparts has been emphasized and, for these researchers, network management mainly consists of allocating resources to different relationships (Ritter, Wilkinson, & Johnston, 2002). Different relationship-specific tasks, such as exchange and coordination aimed at initiating, using, developing, routinizing and dissolving a relationship, have been pointed out (Hoang & Antoncic, Reference Hoang and Antoncic2003; Ritter, Wilkinson, & Johnston, 2004; Slotte-Kock & Coviello, Reference Slotte-Kock and Coviello2009). In addition, the benefits of having a central position in a network have also been highlighted (see e.g., Hoang & Antoncic [Reference Hoang and Antoncic2003] for a review). Network management has been associated to the process of becoming an insider in a relevant network of an industry (Hoang & Antoncic, Reference Hoang and Antoncic2003; Johansson & Vahlne, Reference Johnson and Kaplan2009; Slotte-Kock & Coviello, Reference Slotte-Kock and Coviello2009). Moreover, lastly, different stages in the process of network evolution have been identified (Hoang & Antoncic, Reference Hoang and Antoncic2003; Slotte-Kock & Coviello, Reference Slotte-Kock and Coviello2009). Over time networks become too complex to be adapted and aligned to different firms’ resource challenges (Hite & Hesterly, Reference Hite and Hesterly2001), and network management is focussed on how relationships change and why change occurs (Slotte-Kock & Coviello, Reference Slotte-Kock and Coviello2009).

A major critique of this research is that nearly all of them treat relationships as unconnected and derive strategies for the individual relationship and not for the network (Ritter, Wilkinson, & Johnston, 2004). Focussing on individual’s networking activities can only help us to understand how firms obtain private benefits, that is, the benefits a firm can earn unilaterally by picking up skills from its partner and applying them to its own activity (Khanna, Ranjay, & Nohria, Reference Khanna, Ranjay and Nohria1998). Common benefits accruing to each partner in a network from the collective application of the learning that all the firms go through as a consequence of being part of the same network (Khanna, Ranjay, & Nohria, Reference Khanna, Ranjay and Nohria1998), can also be important. However, with the exception of the work by Ritter, Wilkinson, and Johnston (2002, 2004), less is known about the way firms in a network actively codevelop management activities to effectively materialize common benefits. These authors, on the basis of task classifications in general management literature, suggested four network management tasks to effectively comanage business networks: planning, organizing, staffing and controlling (Ritter, Wilkinson, & Johnston, 2002, 2004). Ritter, Wilkinson, and Johnston (2004) recognize that in business networks, firms participate in a self-organizing process in which strategy can emerge in a bottom-up manner from the microinteractions taking place among firms involved (Wilkinson &Young, 2002), however, they discuss the importance of managing for a planned network strategy (Ritter, Wilkinson, & Johnston, 2004). Their main argument for the deliberate/planned network strategy is the need to integrate the contributions from different actors in the network in order to develop common benefits (Ritter, Wilkinson, & Johnston, 2004). However, there are other situations in which firms cannot plan their network strategy. They cannot select their network partners or cannot influence their behaviour. In these situations, the network management tasks identified by Ritter, Wilkinson, and Johnston are not entirely appropriate (Ritter, Wilkinson, & Johnston, 2002, 2004; Ritter & Gemünden, 2003). In these situations, all firms in a network will be simultaneously involved in its ongoing management, and the resulting strategy is coproduced by their actions. In this work, we focus specifically on this situation that has not been addressed in the literature; precisely because in business networks where informal ties prevail, it is more usual for firms to face situations where it is difficult a priori to identify their network strategy. Consequently, in this paper we propose slightly different network management tasks to the ones proposed by Ritter, Wilkinson, and Johnston (2002, 2004). We examine the following tasks below: interfirm knowledge sharing, resource sharing, coordination, conflict resolution and adaptation between network members. We refer to these network management tasks as interfirm network management activities (INMAs). Thus, our first supposition in this paper is that INMAs help create an effective coworking environment that enables firms to use the potential shared benefits of networking to enhance their own performance by facilitating their adaptation to customer needs. We adopt a marketing focus (Helfert, Ritter, & Walter, Reference Helfert, Ritter and Walter2002) placing network firms’ customers satisfaction as an important element in determining common benefits. We are aware that other strategic elements may influence common benefits and justify the development of INMAs like, for example, technological learning, but they lie outside the scope of this work. Given the lack of research in this area, our first research question is:

Inspired by the contingency perspective (Chandler, Reference Chandler1962; Miles & Snow, Reference Miles, Snow and Sharfman1978; Miller & Friesen, Reference Miller and Friesen1982; Covin & Slevin, Reference Covin and Slevin1989), in this research, we argue that a company’s ability to engage in INMA will depend, in part, on its organizational resources. INMA tend to be resource-consuming activities, therefore the development of INMA will be, to some extent, limited by its resource base. Firms with abundant resources may have a greater capacity than those with sparse resources to engage in INMA. Although different variables have been defined as proxies of firm’s resources (e.g., Covin & Slevin, Reference Covin and Slevin1989), as others before in the network context we use size and age (Håkansson, Reference Håkansson1982). However, a negative effect may be also identified if we use these variables. Bigger and older firms usually develop routines that diminish their flexibility to respond to changes required by INMA to adapt to customer needs (Autio, Sapienza, & Almeida, Reference Autio, Sapienza and Almeida2000). Thus, our second research question is:

The contingency perspective in management also argues for the need to consider environmental characteristics as important determinants of management activities (Chandler, Reference Chandler1962) and has received substantial empirical support (Walter, Kellermanns, Floyd, Veiga, & Matherne, Reference Walter, Kellermanns, Floyd, Veiga and Matherne2013). Past research makes it clear that the nature of industries evolves over time through their life cycle (e.g., Levitt, Reference Lechner, Dowling and Welpe1965; Grant, Reference Grant2010). Contingency theory suggests that the management elements that determine firm adaptation to customer needs will be reconfigured as the life cycle shifts from one stage to another. Although the literature contains a significant body of research supporting this influence (Karniouchina, Carson, Short, & Ketchen, Reference Karniouchina, Carson, Short and Ketchen2013), none of these studies have accounted for the potential effects of changes in life cycle stages on comanagement activities such as INMAs. Consequently, our third research question is:

By highlighting the importance of INMAs, this study extends previous network management research mainly focussed on relationship-specific tasks and cross-relational tasks (Ritter, Wilkinson, & Johnston, 2004) to include insights into how firms in a network develop conjoint bottom-up management activities. Moreover, gaining additional insights into how firms contingency variables (size and industry life cycle stages) can contribute to the development of INMAs will enable us to better understand firms’ networking activities from a managerial perspective.

Furthermore, this study also provides suggestions for researchers when considering variables like industry life cycle, company age and size as control variables. In short, we propose a conceptual model to explain firm performance that relates age, size and industry life cycle with INMA and INMA with firm performance. The following section presents the theoretical background and the relationships between the structural factors studied and INMA. Then, the method for analysing our hypotheses is explained, followed by a discussion of the results. Finally, the conclusions, the implications, the limitations and proposed future research developments are presented.

INMAs and firm performance

In business networks, where informal ties prevail, the managerial challenge is that the firm mainly has to cope with managing interactions taking place in multiple relationships, which may be with partners not entirely of the firm’s choosing and have been in operation for some time. Therefore, each partner has a history that exerts an influence on how things are done (Ritter, Wilkinson, & Johnston, 2004). In these situations, firms need to develop different cross-relational tasks to the ones proposed by Ritter, Wilkinson, and Johnston. We have identified five INMAs firms in a network need to perform to successfully meet customer’s needs: interfirm knowledge sharing, resource sharing, coordination, adaptation and conflict resolution (Helfert, Ritter, & Walter, Reference Helfert, Ritter and Walter2002).

Interfirm knowledge sharing is defined as the set of activities performed jointly by firms in the network enabling them to obtain valuable information from their customers and conjointly develop solutions for improving their offerings. These activities enable network partners to streamline the flow of customer information across organizational boundaries (Shih, Hsu, Zhu, & Balasubramanian, Reference Shih, Hsu, Zhu and Balasubramanian2012), in turn improving firm’s agility and adaptability to new customer needs (Robson, Skarmeas, & Spyropoulou, Reference Robson, Skarmeas and Spyropoulou2006). Knowledge sharing within a network allows a firm to acquire information about its relationship partners, including their resources, needs, capabilities, strategies and other relationships (Johanson & Vahlne, Reference Johnson and Kaplan2009). Such information-sharing activities allow organizations to expand their customer knowledge pool, deliver value-added products or services, detect emerging opportunities and capture business benefits in a hypercompetitive business environment (Shih et al., Reference Shih, Hsu, Zhu and Balasubramanian2012). The process of creating knowledge is not separate from the other activities in business relationships; rather it is embedded in them. Knowledge accrues not only from the firm’s own activities, but also from the activities of its partners, and as those partners also have other relationship partners with whom their activities are coordinated, the firm is indirectly engaged in a knowledge creation process that extends far beyond its own horizon. Thus, a network of business relationships provides a firm with an extended knowledge base (Kogut, Reference Kohli and Jaworski2000). Effective knowledge-sharing activities enable network partners to streamline the flow of customer and market information, money and products across organizational boundaries, in turn improving the agility, adaptability and predictability of the network. These activities are a critical factor for collaborative resource coordination, allocation and integration across different members of the network (Kim, Umanath, Kim, Ahrens, & Kim, Reference Kim, Umanath, Kim, Ahrens and Kim2012).

In addition to these practices, business networks offer their members a portfolio of services designed to overcome the competitive weaknesses of individual firms. Services shared among members could range from negotiating and purchasing from suppliers, marketing, personnel development, to financial services, quality management, inventory optimization and market research. Each network is able to define the services most relevant to its members (Wegner & Padula, Reference Wegner and Padula2010).

Interorganizational coordination refers to synchronization of partners’ actions (Mohr & Nevin, Reference Mohr and Nevin1990). Network coordination can be seen as routines for integrating network activities (Löfgren, Tolstoy, Sharma, & Johanson, Reference Löfgren, Tolstoy, Sharma and Johanson2008). Industrial Marketing and Purchasing project studies show that relationships usually involve a number of managers who work together to coordinate their firms’ activities and create interrelated routines (Cunningham & Homse, Reference Cunningham and Homse1986). This coordination comprises the establishment, use and control of formal rules and procedures and the exertion of informal influence (Helfert, Ritter, & Walter, Reference Helfert, Ritter and Walter2002). Grandori and Soda (Reference Grandori and Soda1995) cite a set of practices that involve the planning, communication and evaluation of strategies. These functions must be modified to suit the dynamics of networks, which are kept in operation by constant negotiation processes. Moreover, evaluation of the results provides information that feeds back to the management of the network and should result in changes (Wegner & Padula, Reference Wegner and Padula2010). Awareness that the network partner may face disadvantages in return for defective behaviour motivates the actor to fulfil the implicit and explicit rules of networking (Fink & Kessler, Reference Fink and Kessler2010).

Adaptation refers to the activities firms must adopt to meet partners’ special needs or the ability to adapt to new circumstances (Helfert, Ritter, & Walter, Reference Helfert, Ritter and Walter2002). Adaptation processes include relationship-specific investments in areas such as technology, products/services, manufacturing processes, logistics, administration, employee qualification or financing (Hallén, Johanson, & Seyed-Mohgamed, Reference Hallén, Johanson and Seyed-Mohamed1991; Claycomb & Frankwick, Reference Claycomb and Frankwick2010). Harrigan (Reference Harrigan1988) showed that partnerships are more likely to succeed when partners possess complimentary missions and resource capabilities. Compatibility in terms of resources is the key issue for performance outcomes. Therefore, coordinating and adapting the activities of a network will help to make resource compatibility a source of superior performance.

The use of constructive conflict resolution mechanisms extends the notion of coordination because these mechanisms address extraordinary, nonstandard situations, which are bound to occur in every long-term relationship (Ruekert & Walker, Reference Ruekert and Walker1987). Interaction/network theory declares that organizations linked by cooperative interaction processes employ other noncontractual processes associated with conflict, coexistence, collusion and competition (McLoughlin & Horan, Reference McLoughlin and Horan2000). In relationships characterized by a desire to establish and maintain long-term, collaborative efforts, managers favour productive conflict resolution mechanisms because they are less volatile. Constructive conflict resolution requires a timely reaction to conflict, a readiness to compromise and a sense of justice. Constructive mechanisms contribute to a relationship, strengthen each firm’s identification with the other, and increase cooperation. Firms developing long-term, collaborative relationships engage in joint problem solving because integration satisfies more fully the needs and concerns of both parties (Claycomb & Frankwick, Reference Claycomb and Frankwick2010). Joint problem solving to resolve conflict leads to mutually satisfactory solutions, thereby enhancing relationship success (Mohr & Spekman, Reference Morgan and Hunt1994).

Successful relationships tend to exhibit processes characterized by high levels of joint participation, cooperation, effective communication and productive conflict resolution. Consequently, in this paper we propose that network-driven performance is associated to the development of INMAs.

Methodology

The purpose of this study is to analyse the role of INMAs in firm performance. In addition, we study the influence of firm size, age and industry life cycle stage on the development of INMAs. As such, the current study involves a multiindustry empirical examination of firms. Data were gathered from a sample of Spanish companies operating in several industries and belonging to an interfirm network. According to Grant (Reference Grant2010), it is likely that an industry will be at different stages of its life cycle in different countries. Therefore, it is advisable to restrict the analysis to only one country, in order to allow comparisons between industries.

Firms were selected from 2010 Dun and Bradstreet Database. Companies had to belong to a network; understanding network as informal relationships among at least three independent companies, in such a way that all the companies have focal relationships with and know the other companies and their activities inside the network (Schoonjans, Van, Cauwenberge, & Bauwhede, Reference Schoonjans, Van Cauwenberge and Bauwhede2013). In addition, firms could not be subsidiary or affiliated companies. Only independently owned and operated firms were included in our sample. This process gave a total population of 9,439 companies. The field research was carried out during the second quarter of 2010 and the final sample consisted of the 400 companies that responded to the questionnaire.

For the field research, interviewee collaboration was requested, together with confirmation of the e-mail address. After the questionnaire had been sent out, follow-up contact was made by telephone to increase the response rate. The questionnaire was posted on the internet and an e-mail with a link to it was sent to each manager. Table 1 summarizes the main characteristics of the sample.

Table 1

Characteristics of the sample

To test for nonresponse bias, the responses of early and late respondents were compared. Analysis of the t-test showed no significant differences (p=.05 level), indicating an absence of nonresponse bias (Armstrong & Overton, Reference Armstrong and Overton1977).

Measuring instruments

The current study relies on previous research for items to measure key constructs. Items were adapted from previous studies by changing words and sentences to enhance understanding in the Spanish context. Table 2 displays specific items used to measure the constructs and their respective factor loadings and t-values.

Table 2

Constructs, measurement items and reliability and validity tests

Industry’s life cycle stage

Beal and Lockamy (Reference Beal and Lockamy1999) used the following measures to identify industry life cycle stage: (1) growth in the industry’s sales during the past 5 years; (2) level of demand for the industry’s products; (3) stage of development of the industry’s products; (4) level of diffusion of information about the industry’s products; (5) plant capacity of the industry’s firms over the past 5 years; (6) current price levels of the industry’s products; (7) growth in the different types of distribution channels for the industry’s products over the past 3 years; and (8) level of the industry’s advertising expenditures over the past 3 years. Following their procedures, each author, based on individual analyses of the respondents, assigned an industry life cycle stage to each of the firms: growth or maturity. Then a value from 1 to 5 that assessed which phase of the stage the industry was in (1 being the earliest and 5 the latest) was assigned to each company. In total, 119 firms were assigned to the growth stage and 279 firms to the maturity stage. Two firms could not be assigned owing to missing data.

Company age

Company age was measured by subtracting the year of the field work (2010) from the year of incorporation.

Company size

Company size was measured through number of employees.

INMAs

An adaptation of the scale proposed by Helfert, Ritter, and Walter (Reference Helfert, Ritter and Walter2002) was used.

Company performance

In situations where firms are hesitant to provide objective performance data, collecting subjective data provides researchers with a better ability to understand the values that a manager may place on performance (Hult et al., Reference Hult, Ketchen, Griffith, Chabowski, Hamman, Dykes, Pollitte and Cavusgil2008). There is evidence to suggest that subjective and objective measures are positively associated (Shoham, Reference Shoham1998) and that subjective measures of performance can accurately reflect objective measures (Lumpkin & Dess, Reference Lumpkin and Dess2001). Furthermore, management assessments of a firm’s performance appear to be guided more by their subjective perceptions than by objective measures (Madsen, Reference Madsen1989). These arguments would seem to support the adoption of subjective measures to assess international performance. Furthermore, Johnson and Kaplan (Reference Johanson and Vahlne1987) outlined the limitations of economic measures and proposed that a selection of noneconomic indicators should be employed. These measures should be based on organizations’ strategies, and include measures of manufacturing, marketing, research and development. Thus, to measure international performance, we adopted a subjective approach in order to improve the response rate. Globally, seven items were used to measure recent performance.

Validity and reliability of the scales

As the aim of our analysis is to describe the validity of indicators as measurement instruments of INMA and performance scales, the confirmatory initial model was adjusted following the indications of Jöreskog and Sörbom (Reference Jöreskog and Sörbom1993). Items INMACon3 (Δ=−0.13, t=−3.96 p<.001) and INMAIks3 (Δ=0.14, t=2.68 p<.01) were eliminated from the scale because they did not reach a λ of 0.5. The validity analysis results show good fit indexes. Table 2 displays the list of items, their sources, their respective standardized factor loadings and t-values, and results of reliability and validity tests. The positive and significant loadings confirm convergent validity of our measures. Results also show α reliability, composite reliability and average variances extracted.

In order to test the discriminant validity between the scales, the confidence interval test was used (Anderson & Gerbing, Reference Anderson and Gerbing1988). According to this test, the value ‘1’ should not appear in the confidence interval of the correlations between the scales in the same level of analysis. Table 3 shows the results of this test, which were satisfactory in all cases.

Table 3

Discriminant validity tests

Results and Discussion

To test the conceptual model, we use a structural equation modelling approach. In order to test Hypothesis 4, the sample was divided in two parts according to whether the companies were in the growth or maturity stage of their industry life cycle. This procedure also enables Hypotheses 1–3 to be tested in two different industrial contexts and evaluate if there is any difference in the relations proposed according to industry life cycle stage. Consistent with prior research (Marks & Kamins, Reference Marks and Kamins1988), the INMA measurement scale was narrowed down averaging the items in the construct. Table 4 shows the descriptive statistics and correlations, and Figure 1 displays the results of the structural models analyses.

Figure 1

Models of effects of industry life cycle stage, age and size on main interfirm network management activities (INMA) and impact of these activities on international new venture performance

Table 4

Descriptive statistics and correlations

Note: *p<.05; **p<.01.

The results show differences according to the life cycle stage considered. Only the relation between INMA and performance has been confirmed in both samples. This finding points to the need to consider the industrial environment when analysing firms’ networking decisions because the situations they face differ in mature or growing industries. As expected, the results show a positive relation between INMA and performance in both cycle stages (Δ=0.38, t=3.31 p<.001 for growth stage and Δ=0.43, t=5.98 p<.001 for maturity stage). This result supports Hypotheses 1 and underlines the importance of firms getting involved in the development of INMAs to manage their networks. This finding contributes to the literature on firm’s network management (Möller & Halinen, Reference Mohr and Spekman1999; Ritter, Wilkinson, & Johnston, 2002, 2004; Ritter & Gemünden, 2003) by showing the importance for firms of developing cross-relational management tasks, which do not necessarily respond to a strategy planned by top management. Ritter et al. (Ritter, Wilkinson, & Johnston, 2002, 2004; Ritter & Gemünden, 2003) recognize that in some situations it may not be possible a priori to determine the firm’s network strategy, because it will emerge out of the interactions between firms in the network. This situation, however, has not been specifically contemplated by these authors. We have also confirmed the influence of developing INMAs on firm performance regardless of the life cycle in the industry in which the firm is operating. Thus, the results encourage us to propose that INMAs could be included in the firm’s network management capability construct developed by Ritter et al. (Ritter, Wilkinson, & Johnston, 2002, 2004; Ritter & Gemünden, 2003). The network management capability is referred to ‘as the firm’s capability to mobilize and coordinate the resources and activities of other actors in the network’ (Möller & Halinen, Reference Mohr and Spekman1999, p. 417). Ritter, Wilkinson, and Johnston (2002, 2004) analyse the degree of network management capability through the development of relationship-specific and cross-relational management tasks. Our findings also encourage us to consider INMAs when analysing a firm’s network management capability because they focus on nondeliberate aspects of network management. The importance of our proposal is justified by the influence of INMAs on the performance of firms in the network. Furthermore, it could also be thought that in the cases of firms that can define a priori their network strategies, the development of INMAs would aid the introduction of these strategies when firms involved in a network have different goals. A firm’s network management ability can only be understood in an ongoing, firm-wide process (Ritter, Wilkinson, & Johnston, 2004). Consequently, and based on seminal Mintzberg’s studies, we argue that the firm’s real network strategy will be the outcome of deliberate, intentional or rational cross-relational management tasks and of the result of developing INMAs to align its deliberate network strategy with the rest of network members (Mintzberg & Quinn, Reference Mintzberg and Quinn1991).

Furthermore, the development of INMAs can be viewed as a network-specific competence that varies among networks and can be an important source of competitive advantage for the network as a whole and for each firm in the network.

The findings in this work are also in line with those reported by Prashantham and Young (2011). These authors point out the importance of tie strength in the processes of assimilating and exploiting new knowledge. Our results, however, also show that stronger ties influence the processes of acquiring and transforming new knowledge. In fact, as argued in this work, the development of INMAs helps to improve firms’ information bases and facilitates their transformation. In contrast, Prashantham and Young (2011) indicate that in these stages of developing new knowledge weak ties would be more influential. In short, our findings appear to indicate that it is the development of INMAs that influences firms’ absorption capacity, understood as a firm’s capacity to uptake and integrate new external knowledge (Zahra & George, 2002), rather than tie strength. However, we want to emphasize that our results only point in this direction, because in this work we have not tested the relationship between the development of INMAs and a firm’s absorption capacity.

Our Hypothesis 2 proposed the existence of a negative effect of company size on INMAs. The results of the analyses show that the relationship has been confirmed in the firms classified as being in the growth stage (Δ=−0.21, t=−2.03 p<.05) but not in the case of the companies in the maturity stage (Δ=0.14, t=1.08). According to the results in growing industries, bigger companies discourage the development of INMAs. When the market is growing, big companies rely on the advantages of their size to make the most of good market conditions, promoting insularity and resisting interaction to other network members. This result is consistent with a large part of the literature on networks, which demonstrates their importance in bridging information gaps (Slotte-Kock & Coviello, Reference Slotte-Kock and Coviello2009; Freeman, Hutchings, Lazaris, & Zyngier, Reference Freeman, Hutchings, Lazaris and Zyngier2010), in providing small- and medium-sized firms with market and technology knowledge (Slotte-Kock & Coviello, Reference Slotte-Kock and Coviello2009; De Clercq, Sapienza, Yavuz, & Zhou, Reference De Clercq, Sapienza, Yavuz and Zhou2012), and in facilitating these firms’ growth (Hite & Hesterly, Reference Hite and Hesterly2001). Our results, however, do not allow us to confirm the same pattern of behaviour in the case of firms in mature industries.

Hypothesis 3 suggests the existence of a negative effect of company age on INMAs. Our results do not show that age can facilitate INMAs in any of the stages. Consequently, Hypothesis 3 cannot be confirmed. Contrary to what is commonly accepted in networking literature, the development of INMAs does not appear to need the support of internal organizational processes. Companies’ reluctance to change in adulthood either hinders or fosters network adaptation activities. Young companies’ need for resources does not appear to encourage them to participate in INMAs more than mature firms. This result, in line with the findings in Hite and Hesterly (Reference Hite and Hesterly2001), shows the importance of informal networks regardless of firm age. However, Hite and Hesterly (Reference Hite and Hesterly2001) point out that firm age does influence the structural characteristics of networks, whereas our study indicates that firm age cannot be considered an antecedent of the development of INMAs.

The results of our study confirm the influence of the development of INMAs on the performance of firms in a network regardless of the life cycle of the industry where they operate. Our results are not so conclusive, however, for the analysis of whether industry life cycle can be considered a contingent variable that influences which firms become involved in developing INMAs. Thus, in the growth stage, the relation between position in that stage and INMA shows a negative and significant effect, thereby indicating that this stage in the industry life cycle has a negative influence on INMAs. The results seem to suggest that when the market is growing, companies focus on obtaining the advantages of the stage. In the growing stages, individuals in firms are relevant resources and their interpretation of the environment is important (Maignan & Lukas, Reference Maignan and Lukas1997). The search for efficiency in production and distribution is the determinant that guides networking in those industries. In the maturity stage, however, there is no significant relation with INMA. Consequently, Hypothesis 4 is only partially confirmed. This result could be owing to the fact that firms in a mature industry do not introduce new resources into the market.

Conclusions

Evidence shows that business networks generate valuable benefits. INMA constitutes an additional objective for firms involved in business relationships, as a way of obtaining benefits in the shape of high levels of performance. In addition, INMA goes further than the leader company in the network and involves the participation of all members in the activities needed to develop network management and obtain its benefits.

Most previous research has focussed on industry, age and size as control variables that should not be influencing the effect of the variables studied. Our research has considered these variables as factors that directly foster or inhibit the development of INMA and so, the study indicates the need for future networking studies to consider the influence of these variables in their hypotheses.

According to our results, added benefits of networking could be obtained by adopting comanagement activities. It is to be expected that developing INMA helps companies to extend their customer knowledge base. In a general sense, INMA development requires partners in a network to know each other’s capabilities and share the same vision of the collaboration process as they work towards the common goal. Companies in networks not only share new market and customer information, but also share procedures that may help them to integrate the new knowledge in their knowledge base and exploit it. Therefore, a future line of research would be to explore the implications of INMA development for companies’ absorption ability to further our understanding of the importance of networks in companies’ success.

This study shows the importance of INMAs in a firm’s network capability, because they are related to the problems firms have to face when rolling out their network strategy. However, the development of INMAs is also a network-specific capability, which varies among networks. From our research, we can conclude that the development of INMAs can be an important source of competitive advantage for the network as a whole. This capability has received relatively scant programmatic attention within network theory; therefore we suggest that future research in this area is needed to better understand the value of different types of networks. Different kinds of networks are typically assumed to function differently and have different capacities for extracting resources (Hite & Hesterly, Reference Hite and Hesterly2001; Lechner, Dowling, & Welpe, Reference Levitt2006). Hite and Hesterly (Reference Hite and Hesterly2001) have distinguished between identity-based networks and calculative networks. Identity-based networks involve some type of personal identification with the other actor that motivates or influences economic actions. Calculative networks are primarily motivated by expected economic benefits. Different types of calculative networks can also be identified by the economic goals. Lechner, Dowling, and Welpe (Reference Levitt2006) suggest that reputational networks, coopetition networks, marketing networks and technological networks are most important types of calculative networks.

From a managerial point of view, considering the structural factors analysed in our research could help firms to be aware of the forces that are driving their decisions and review the behaviours that, as in the case of being in the industry life cycle growth stage, are moving the company away from capabilities that could provide them with superior performance.

The results seem to suggest that research should consider not only the differences between particular industries, but also the differences between industry life cycle stages. This opens a new opportunity for generalizing results. In order to control for industry effects, most studies focus on a few industries, limiting the generality of their results. Our results show that differences between industries can also be observed at the life cycle stage, providing a higher degree of generalization because samples can be constituted by individuals from several industries. This approach also facilitates sampling. Although it might be difficult to obtain large enough samples from only one industry it seems easier to obtain answers from more respondents based on a life cycle.

These conclusions should be considered in the light of some limitations related to the method followed in our research. The sample for testing the hypotheses proceeded from a sample of Spanish companies thus, cultural and environmental factors affecting the activities inside the networks cannot be ruled out. Furthermore, although we received 400 responses to our questionnaire, the response rate was only 4.2%, and therefore insufficient to generalize the results to the population. Consequently, additional research in other countries and with representative samples would be helpful in order to generalize the results.

We adopted a global perspective of looking at the network asking interviewees to refer their answers to the main network to which they belonged. Consulting only one member from a network for information on its activities could bias the data because they came from only one perspective of the situation. Nevertheless, as management activities are a shared behaviour for all members of the network, major differences in answers from respondents in the same network are not expected. A way of improving the collection of data from a network would be to identify all its members and interview all the agents involved in the relationships.

Although structural equation models allow testing of direct causal relations in a nonexperimental situation, there is still the problem of when an activity is implemented and when it is measured. Further research using longitudinal data is needed in order to test if the relationships established in this study have been affected by the cross-sectional design.